Motion correction of images corrupted by multiple motion sources

ABSTRACT

The present disclosure relates to dividing image data obtained from a scan (e.g. MRI) of an object into two or more sets of data corresponding do unique motion patterns and/or motion sources. Each of the two or more sets of data can be corrected using an appropriate correction technique. One appropriate correction techniques includes generating kernels for each divided imaging dataset using center and adjacent slice information to correct for through-plane and in-plane artifacts.

CROSS-REFERENCE TO CO-PENDING APPLICATION

The present application relates to and claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 63/283,659 filed onNov. 29, 2021, the contents of which are incorporated herein byreference.

BACKGROUND

Motion artifacts are a common problem in medical imaging, such asmagnetic resonance imaging (MRI). In a given scan of a body, there maybe different sources of motion (e.g. heart, lungs, throat) having uniquemotion characteristics. For example, cardiac motion and respiratorymotion are both approximately periodic (e.g. 60-72 beats per minutes,12-15 cycles of inspiration/expiration), whereas swallowing and coughingmay be aperiodic. Motion correction techniques typically aim touniformly correct all motion at once, without considering the variationsin motion that can occur for different regions of the body. The resultis a correction that is often sub-optimal, and may contain residualartifacts that have not been fully corrected.

SUMMARY

The present disclosure relates to an imaging apparatus, including, butnot limited to: a plurality of detectors configured to capture imagingdata from a scan of an object; and circuitry configured to divide theimaging data into a first set corresponding to a first region of theobject and a second set corresponding to a second region of the object,wherein the second region is different than the first region, the firstregion has a first motion pattern, and the second region has a secondmotion pattern different than the first motion pattern, apply a firstcorrection process to the first set, apply a second correction processto the second set, the second correction process being different thanthe first correction process, and generate an image including the firstregion and the second region, the image being generated based on aresult of the first correction process and the second correctionprocess.

The present disclosure also relates to a correction method, including,but not limited to: dividing imaging data into a first set correspondingto a first region of an object and a second set corresponding to asecond region of the object, wherein the second region is different thanthe first region, the first region has a first motion pattern, and thesecond region has a second motion pattern different than the firstmotion pattern; applying a first correction process to the first set;applying a second correction process to the second set, the secondcorrection process being different than the first correction process;and generating an image including the first region and the secondregion, the image being generated based on a result of the firstcorrection process and the second correction process.

The present disclosure also relates to a non-transitorycomputer-readable storage medium storing computer-readable instructionsthat, when executed by a computer, cause the computer to perform amethod including, but not limited to, dividing imaging data into a firstset corresponding to a first region of an object and a second setcorresponding to a second region of the object, wherein the secondregion is different than the first region, the first region has a firstmotion pattern, and the second region has a second motion patterndifferent than the first motion pattern, applying a first correctionprocess to the first set, applying a second correction process to thesecond set, the second correction process being different than the firstcorrection process, and generating an image including the first regionand the second region, the image being generated based on a result ofthe first correction process and the second correction process.

The foregoing paragraphs have been provided by way of generalintroduction, and are not intended to limit the scope of the followingclaims. The described embodiments, together with further advantages,will be best understood by reference to the following detaileddescription taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the disclosure and many of the attendantadvantages thereof will be readily obtained as the same becomes betterunderstood by reference to the following detailed description whenconsidered in connection with the accompanying drawings, wherein:

FIG. 1 is a schematic of an MRI apparatus;

FIG. 2A shows an image of an object having artifacts due to swallowingin one region and artifacts due to cardiac motion in a different region;

FIG. 2B shows nine images of the object from FIG. 2A taken by ninecorresponding coils;

FIG. 2C shows nine coil sensitivity maps corresponding to the nineimages from FIG. 2B;

FIG. 2D shows images taken from coils sensitive to artifacts due toswallowing and images taken from coils sensitive to artifacts due tocardiac motion;

FIG. 3A shows an image of an object without data rejection;

FIG. 3B shows an image of the object with data rejected from four coils;

FIG. 3C shows an image of the object with data rejected from five coils;

FIG. 4 illustrates synthesizing a kernel using a center slice and itsadjacent slices;

FIG. 5A shows a motion corrupted image of an object;

FIG. 5B shows the image from FIG. 5A after being synthesized withoutadjacent slice information;

FIG. 5C shows the image from FIG. 5A after being synthesized withadjacent slice information;

FIG. 6A shows a motion corrupted image of an object;

FIG. 6B shows the image from FIG. 6A after being synthesized withoutadjacent slice information;

FIG. 6C shows the image from FIG. 6A after being synthesized withadjacent slice information; and

FIG. 7 shows a flowchart of an exemplary method described herein.

DETAILED DESCRIPTION

The terms “a” or “an”, as used herein, are defined as one or more thanone. The term “plurality”, as used herein, is defined as two or morethan two. The term “another”, as used herein, is defined as at least asecond or more. The terms “including” and/or “having”, as used herein,are defined as comprising (i.e., open language). Reference throughoutthis document to “one embodiment”, “certain embodiments”, “anembodiment”, “an implementation”, “an example” or similar terms meansthat a particular feature, structure, or characteristic described inconnection with the embodiment is included in at least one embodiment ofthe present disclosure. Thus, the appearances of such phrases or invarious places throughout this specification are not necessarily allreferring to the same embodiment. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more embodiments without limitation.

The present disclosure is related to generating a motion corrected imagefrom an imaging scan by utilizing a process that takes into accountdifferent motion sources and/or patterns, and corrects for each motionseparately. This allows for a more complete correction compared totechniques that jointly correct artifacts from all motion sources, sincemotion correction is customized to the characteristics of spatiallylocalized motion.

In one embodiment, it can be appreciated that the present disclosure canbe viewed as a system. While the present exemplary embodiments willrefer to an MRI apparatus, it can be appreciated that other systemconfigurations can use other medical imaging apparatuses (e.g. computedtomography apparatus).

Referring now to the drawings, FIG. 1 is a block diagram illustratingoverall configuration of an MRI apparatus 1. The MRI apparatus 1includes a gantry 100, a control cabinet 300, a console 400, a bed 500,and radio frequency (RF) coils 20. The gantry 100, the control cabinet300, and the bed 500 constitute a scanner, i.e., an imaging unit.

The gantry 100 includes a static magnetic field magnet 10, a gradientcoil 11, and a whole body (WB) coil 12, and these components are housedin a cylindrical housing. The bed 500 includes a bed body 50 and a table51.

The control cabinet 300 includes three gradient coil power supplies 31(31 x for an X-axis, 31 y for a Y-axis, and 31 z for a Z-axis), a coilselection circuit 36, an RF receiver 32, an RF transmitter 33, and asequence controller 34.

The console 400 includes processing circuitry 40, a memory 41, a display42, and an input interface 43. The console 400 functions as a hostcomputer.

The static magnetic field magnet 10 of the gantry 100 is substantiallyin the form of a cylinder and generates a static magnetic field inside abore into which an object such as a patient is transported. The bore isa space inside the cylindrical structure of the gantry 100. The staticmagnetic field magnet 10 includes a superconducting coil inside, and thesuperconducting coil is cooled down to an extremely low temperature byliquid helium. The static magnetic field magnet 10 generates a staticmagnetic field by supplying the superconducting coil with an electriccurrent provided from a static magnetic field power supply (not shown)in an excitation mode. Afterward, the static magnetic field magnet 10shifts to a permanent current mode, and the static magnetic field powersupply is separated. Once it enters the permanent current mode, thestatic magnetic field magnet 10 continues to generate a strong staticmagnetic field for a long time, for example, over one year. In FIG. 1 ,the black circle on the chest of the object indicate the magnetic fieldcenter.

The gradient coil 11 is also substantially in the form of a cylinder andis fixed to the inside of the static magnetic field magnet 10. Thisgradient coil 11 applies gradient magnetic fields (for example, gradientpulses) to the object in the respective directions of the X-axis, theY-axis, and the Z-axis, by using electric currents supplied from thegradient coil power supplies 31 x, 31 y, and 31 z.

The bed body 50 of the bed 500 can move the table 51 in the verticaldirection and in the horizontal direction. The bed body 50 moves thetable 51 with an object placed thereon to a predetermined height beforeimaging. Afterward, when the object is imaged, the bed body 50 moves thetable 51 in the horizontal direction so as to move the object to theinside of the bore.

The WB body coil 12 is shaped substantially in the form of a cylinder soas to surround the object and is fixed to the inside of the gradientcoil 11. The WB coil 12 applies RF pulses transmitted from the RFtransmitter 33 to the object. Further, the WB coil 12 receives magneticresonance signals, i.e., MR signals emitted from the object due toexcitation of hydrogen nuclei.

The MRI apparatus 1 may include the RF coils 20 as shown in FIG. 1 inaddition to the WB coil 12. Each of the RF coils 20 is a coil placedclose to the body surface of the object. There are various types for theRF coils 20. For example, as the types of the RF coils 20, as shown inFIG. 1 , there are a body coil attached to the chest, abdomen, or legsof the object and a spine coil attached to the back side of the object.As another type of the RF coils 20, for example, there is a head coilfor imaging the head of the object. Although most of the RF coils 20 arecoils dedicated for reception, some of the RF coils 20 such as the headcoil are a type that performs both transmission and reception. The RFcoils 20 are configured to be attachable to and detachable from thetable 51 via a cable.

The RF transmitter 33 generates each RF pulse on the basis of aninstruction from the sequence controller 34. The generated RF pulse istransmitted to the WB coil 12 and applied to the object. An MR signal isgenerated from the object by the application of one or plural RF pulses.Each MR signal is received by the RF coils 20 or the WB coil 12.

The MR signals received by the RF coils 20 are transmitted to the coilselection circuit 36 via cables provided on the table 51 and the bedbody 50. The MR signals received by the WB coil 12 are also transmittedto the coil selection circuit 36

The coil selection circuit 36 selects MR signals outputted from each RFcoil 20 or MR signals outputted from the WB coil depending on a controlsignal outputted from the sequence controller 34 or the console 400.

The selected MR signals are outputted to the RF receiver 32. The RFreceiver 32 performs analog to digital (AD) conversion on the MRsignals, and outputs the converted signals to the sequence controller34. The digitized MR signals are referred to as raw data in some cases.The AD conversion may be performed inside each RF coil 20 or inside thecoil selection circuit 36.

The sequence controller 34 performs a scan of the object by driving thegradient coil power supplies 31, the RF transmitter 33, and the RFreceiver 32 under the control of the console 400. When the sequencecontroller 34 receives raw data from the RF receiver 32 by performingthe scan, the sequence controller 34 transmits the received raw data tothe console 400.

The sequence controller 34 includes processing circuitry (not shown).This processing circuitry is configured as, for example, a processor forexecuting predetermined programs or configured as hardware such as afield programmable gate array (FPGA) or an application specificintegrated circuit (ASIC).

The console 400 includes the memory 41, the display 42, the inputinterface 43, and the processing circuitry 40 as described above.

The memory 41 is a recording medium including a read-only memory (ROM)and a random access memory (RAM) in addition to an external memorydevice such as a hard disk drive (HDD) and an optical disc device. Thememory 41 stores various programs executed by a processor of theprocessing circuitry 40 as well as various types of data andinformation.

The input interface 43 includes various devices for an operator to inputvarious types of information and data, and is configured of a mouse, akeyboard, a trackball, and/or a touch panel, for example.

The display 42 is a display device such as a liquid crystal displaypanel, a plasma display panel, and an organic EL panel.

The processing circuitry 40 is a circuit equipped with a centralprocessing unit (CPU) and/or a special-purpose or general-purposeprocessor, for example. The processor implements various functionsdescribed below (e.g. method 700) by executing the programs stored inthe memory 41. The processing circuitry 40 may be configured as hardwaresuch as an FPGA and an ASIC. The various functions described below canalso be implemented by such hardware. Additionally, the processingcircuitry 40 can implement the various functions by combining hardwareprocessing and software processing based on its processor and programs.

As previously mentioned, the present disclosure is related to generatinga high quality image from an image scan by utilizing a process thattakes into account different motion sources and/or patterns. Instead ofuniformly correcting motion artifacts, artifacts from each motion sourceand/or pattern are corrected separately, where different motioncorrection methods can be applied to different motion sources and/orpatterns. For example, in regions near the spine, different motionsources cause artifacts in different regions within the field of view.Swallowing motion in the mouth can cause artifacts in the superiorc-spine region, while cardiac and respiration motion in the chest cancause artifacts in the inferior c-spine region. In such a case, motionfrom the various regions can be corrected separately, then combined toform a composite, motion-corrected image.

The types of motion can generally fall into two categories: predicableand unpredictable. Predictable motion can include motion that isapproximately periodic, non-sporadic, rigid, etc., while unpredictablemotion can include motion that is aperiodic, sporadic, non-rigid, etc.Examples of predictable motion include breathing in the lung region anda beating heart. Examples of unpredictable motion include coughing,swallowing, or sneezing in the throat region.

An image dataset (e.g. MRI dataset) obtained from scanning a field ofview can be divided into different regions of interest having differentmotion characteristics. For example, a first region is near the chestfor cardiac and respiratory motion, a second region is near the throatfor swallowing motion, and a third region is near the head for headmotion. Thereafter, different motion correction methods, specificallysuited for each designated region/anatomy/motion pattern, can beapplied.

In one embodiment, the regions of interest can be defined based on (1)coil sensitivity maps, (2) spatial patches based on anatomy, (3) patchesin k-space exploiting frequency domain signal similarity, or (4) acombination thereof. In cases (1) and (2), each coil/spatial patch viewsa different region, while in case (3), k-space patches along slicedimensions share similar signal information.

In one embodiment of the present disclosure, imaging data is dividedinto multiple sets based on regions affected by different motion usingcoil sensitivity maps and/or spatial patches based on anatomy.Thereafter, each set is separately corrected using an appropriatecorrection technique. For example, unpredictable motion can be correctedusing techniques like COCOA, which find inconsistencies between acquiredk-space data and synthesized data using a convolution kernel (e.g.,GRAPPA), and replaces or rejects inconsistent data to reduce motionartifacts (See, e.g., (1) [Huang 2010]: (Huang et al. Data convolutionand combination operation (COCOA) for motion ghost artifacts reduction.MRM 64:157-166, 2010), and (2) [Huang 2012]: (Huang et al. Advantages ofchannel by channel artifact detection and correction. #3434, ISMRM2012.)), both of which are incorporated herein by reference. Moreover,predictable motion can be corrected using self-navigation methods, whereacquired data itself can be used to estimate motion and correct for it.Of course, other appropriate motion correction methods can be used inother scenarios.

FIG. 2A shows an image of a spine region captured by an MRI apparatususing nine coils, where the image includes artifacts 201 a due toswallowing and artifacts 201 b due to cardiac motion. The artifacts 201a due to swallowing are localized to a different region and have adifferent motion pattern than artifacts 201 b due to cardiac motion.

FIG. 2B shows nine low-resolution images 1 a-9 a acquired separatelyfrom nine corresponding coil elements in the MRI apparatus used duringc-spine imaging of the region from FIG. 2A. The spatial patches based onanatomy can be utilized to know which coils are directed to whichanatomy. For example, it can be made known that the coils used tocapture images 2 a, 5 a, and 6 a are directed towards the throat, andthat the coils used to captures images 3 a, 4, and 9 a are directed tothe heart.

FIG. 2C shows respective coil sensitivity maps 1 b-9 b for each of thecoils used to generate images 1 a-9 a from FIG. 2B. The coil sensitivitymaps 1 b-9 b quantify the relative weighting of signals from differentpoints of origin within the reception area of each coil.

The coil sensitivity maps 1 b-9 b provide a natural segmentation ofregions that may be affected by artifact from different motion sources.For example, referring to FIG. 2D, it can be known that coils used tocaptures images 2 a, 5 a, and 6 a are sensitive to the region withartifacts 201 a due to swallowing, and that coils used to capture images3 a, 4 a, and 9 a are sensitive to the region with artifacts 201 b dueto cardiac motion. Therefore, imaging data collected from the former setof coils can be corrected using a first correction process specializedfor correcting the artifacts 201 a due to swallowing, while imaging datacollected from the latter set of coils can be corrected using a secondcorrection process specialized for correcting the artifacts 201 b due tocardiac motion.

In an embodiment, data rejection can be included. For instance, portionsof data collected from an MRI scan having particular characteristics canbe removed. Examples of characteristics warranting data rejection caninclude motion outside a defined location, motion outside apredetermined frequency range, and/or motion having a particularpattern.

To illustrate the effects of reducing artifacts via data rejection, FIG.3A shows an MRI image, constructed without using data rejection, withartifacts near the neck region 401. FIG. 3B shows the MRI image withdata rejected from four coils that are sensitive to motion near the neckregion 401. FIG. 3C shows the same MRI image with data rejected fromfive coils (including the same four coils from FIG. 3B) that aresensitive to motion near the neck region 401. As can be seen, rejectingdata reduces the motion artifacts near the neck region 401 (at theexpense of reduced signal-to-noise-ratio).

Furthermore, data rejection can be coupled with other navigator-basedmotion correction techniques, which are independent of the type ofmotion. Data having certain motion states can be accepted or rejected.This can look like, for example, rejecting portions of k-space data thatare corrupted with motion. As another example, for periodic motion (e.g.respiration or cardiac pulsations), data from peak inhalation,exhalation, systolic phase, and/or diastolic phase can be rejected.

Navigators can also be employed in other ways. For example, navigatorscan be used to bin data having similar motion states, and corrected foreach motion state separately. As another example, navigators can be usedto estimate motion of a specific body part, and correct for itretrospectively.

Therefore, in one embodiment, the acquisition of imaging data can bepreceded or succeeded by the acquisition of non-imaging data that serveas navigators for motion correction (i.e. data-based navigation). Theuse of non-imaging data is described in Lin, Wei, et al. “Motioncorrection using an enhanced floating navigator and GRAPPA operations.”Magnetic Resonance in Medicine: An Official Journal of the InternationalSociety for Magnetic Resonance in Medicine 63.2 (2010): 339-348; andWelch, Edward Brian, et al. “Spherical navigator echoes for full 3Drigid body motion measurement in MRI.” Magnetic Resonance in Medicine:An Official Journal of the International Society for Magnetic Resonancein Medicine 47.1 (2002): 32-41. The contents of both of those referencesare incorporated herein by reference. External navigators (e.g., camera,respiratory motion sensors, cardiac motion sensors) can be used toestimate motion and correct for it retrospectively. The use of externalnavigators is described in Qin, Lei, et al. “Prospective head-movementcorrection for high-resolution MRI using an in-bore optical trackingsystem.” Magnetic Resonance in Medicine: An Official Journal of theInternational Society for Magnetic Resonance in Medicine 62.4 (2009):924-934; and Todd, Nick, et al. “Prospective motion correction of 3Decho-planar imaging data for functional MRI using optical tracking.”NeuroImage 113 (2015): 1-12. The contents of both of those referencesare incorporated herein by reference. Self-navigation can also be used,where the acquired data itself is used to estimate and correct motion.The use of self-navigation references is described in Pipe, James G.“Motion correction with PROPELLER MRI: application to head motion andfree-breathing cardiac imaging.” Magnetic Resonance in Medicine: AnOfficial Journal of the International Society for Magnetic Resonance inMedicine 42.5 (1999): 963-969; Feng, Li, et al. “XD-GRASP: golden-angleradial MRI with reconstruction of extra motion-state dimensions usingcompressed sensing.” Magnetic resonance in medicine 75.2 (2016):775-788; and Cordero-Grande, Lucilio, et al. “Sensitivity encoding foraligned multishot magnetic resonance reconstruction.” IEEE Transactionson Computational Imaging 2.3 (2016): 266-280. The contents of thosethree references are incorporated herein by reference.

In one embodiment, a slice-based convolution approach can be used tocorrect for non-rigid, through-plane artifacts. A motion corrupteddataset can be divided in k-space based on frequency domain signalsimilarity to generate multiple motion corrupted datasets. New datasetscan then be synthesized from each of the motion corrupted datasets usingat least one convolution kernel, where estimating the kernels includesutilizing information from adjacent slices. The synthesized datasets arecombined in k-space domain to form a motion corrected k-space. A motioncorrected image is obtained from the motion corrected k-space viaFourier transform. In one embodiment, this technique can be coupled withother rigid body motion techniques.

For unpredictable motion, only some phase encoding (PE) lines arecorrupted with motion. Since motion can be through-plane as well asin-plane, adjacent PE lines from both within a slice and adjacent to theslice can be used to synthesize the new datasets. A pseudo-randomizedsampling pattern can be used to acquire data in k-space, ensuring thatadjacent PE lines have no motion or incoherent motion. Such an approachcan suppress motion artifacts by dispersing the error to neighbouringvoxels/PE lines. This can be optimal for synthesizing new motionsuppressed data from motion corrupted data. For example, a convolutionkernel is used to synthesize a new point from a weighted sum of itsneighboring points. If an error due to motion exists in any one point,its effect is dispersed to neighboring points. The result is a reductionin the appearance of the artifact.

Examples of sampling patterns that can be used include shuffle encoding(that uses a pseudo-randomized sampling pattern) or stepped encoding(where k-space data is split into subsets, and the subsets areinterleaved). Sequential sampling pattern is another example of asampling pattern that can be used.

The center k-space of the acquired motion corrupted dataset can be usedto estimate a convolution kernel. This kernel can then be used tosynthesize a new-k-space dataset. In one embodiment, L2 regularizationcan be used in the estimation of the kernel to account for signal tonoise ratio loss due to synthesis. For example, regularization can beperformed as in GRAPPA kernel estimation. One such implementation isdescribed in Liu, Wentao, et al. “Improved parallel MR imaging using acoefficient penalized regularization for GRAPPA reconstruction.”Magnetic resonance in medicine 69.4 (2013): 1109-1114, the contents ofwhich is incorporated by reference.

FIG. 4 shows a center slice (e.g., slice 7) and two adjacent slices(e.g., slices 6 and 8), where each slice has source pixels and discardedpixels, and the center slice has a target pixel. For the kernel size of[3, 6], the target pixel in the center slice is synthesized with sourcepixels within the kernel from slices 6, 7, and 8.

Utilizing information from adjacent slices can reduce in-plane artifactsand improve image quality (compared to using only center sliceinformation, or no kernel at all). FIGS. 5A, 5B, and 5C show a motioncorrupted image where a kernel was not applied, a synthesized imagewithout using adjacent slice information, and a synthesized image usingadjacent slice information, respectively. As can be seen from the whitevertical streak in 501 a and the signal heterogeneity in the cord in 501b-d, the in-plane motion artifacts 501 a-501 d in the sagittal c-spineregion are significantly reduced in FIG. 5C when adjacent sliceinformation is used.

Utilizing information from adjacent slices can also reduce through-planeartifacts (compared to using only center slice information, or no kernelat all). FIGS. 6A, 6B, and 6C shows a motion corrupted image where akernel was not applied, a synthesized image without using adjacent sliceinformation, and a synthesize image using adjacent slice information,respectively. As can be seen by comparing FIGS. 6A-6C, the through-planemotion artifacts 601 a, 601 b in the sagittal c-spine region aresignificantly reduced in FIG. 6C. For example, the signal intensity inthe cord is more homogenous in FIG. 6C than FIG. 6B, and the blackcircle that appears in the middle of the white square in 601 a isreduced in FIG. 6C.

In one embodiment, it can be appreciated that the techniques discussedherein can be viewed as a method. FIG. 7 illustrates a flowchartoutlining a method 700 according to an embodiment of the presentdisclosure.

Step 701 illustrates dividing imaging data into a first setcorresponding to a first region of an object and a second setcorresponding to a second region of the object. The second region isdifferent than the first region, the first region has a first motionpattern, and the second region has a second motion pattern differentthan the first motion pattern. Some examples of possible first andsecond regions are the head, throat, heart, or lungs, although otherregions are possible. Portions of data from the first and second set mayor may not overlap. The first and second motion patterns can be any typeof motion, such as periodic, aperiodic, rigid, non-rigid, sporadic, ornon-sporadic.

In one embodiment, the first motion pattern is predictable, and thesecond motion pattern is unpredictable. In one embodiment, the first andsecond motion pattern are both predictable (or both unpredictable), buthave unique characteristics (e.g. shape, size, frequency) that justifythe usage of different correction processes. In one embodiment, theimaging data can be MRI data acquired from a scan of the object by theMRI apparatus 1.

The dividing in step 701 can be based on a coil sensitivity map, spatialpatches based on anatomy, patches in k-space exploiting frequency domainsignal similarity, or a combination thereof. The dividing can be done tosplit the imaging data based on unique motion characteristics ofdifferent regions within the scanned object. The first and second setsof data can make up the entirety of the imaging data, or a portionthereof. Of course, in other embodiments, more than two sets can becreated from the dividing.

In an embodiment, navigators can be used to identify motion patterns fordetermining how to split the imaging data. For example, if navigatorsdetect a first motion pattern coming from a first set of coils and asecond motion pattern coming from a second set of coils, the first andsecond sets can be split accordingly. In a case such information isalready known or easily predictable, navigators can be omitted.

Step 703 and step 705 are to apply a first correction process to thefirst set and apply a second correction process to the second set,respectively, where the first correction process and the secondcorrection process are different.

Factors such as the first motion pattern, second motion pattern, firstregion of the object, and second region of the object can be consideredwhen determining the correction process to use. For example, COCOA canbe used for sporadic motion in the throat, while self-navigation can beused for periodic motion in the heart or lungs. Of course, othersuitable correction processes (e.g. neural networks) can be used inother scenarios.

Further, if the imaging data was split according to signal similarity inthe k-space, a convolution kernel can be estimated using center sliceand adjacent slice infounation, as previously discussed. The convolutionkernel can then be applied to synthesize a new dataset from the motioncorrupted dataset using the convolution kernel.

Further, as previously noted, data rejection can be applied. If, forexample, a correction process does not work or is not optimal for agiven dataset, all or regions of that dataset can be rejected. Also, aspreviously discussed, navigators can be utilized in one or bothcorrection processes.

Step 703 can be performed before, after, or in parallel to step 705. Ofcourse, more than two correction process can be used in otherembodiments. For example, in a case that the imaging data was split intothree sets, three different correction processes can be used for each ofthe three divided sets.

Step 707 is to generate an image. The separately corrected imaging datacan be combined to form one motion-corrected image. Step 707 can includeperforming a Fourier transform to convert frequency domain data into animage. Thereafter, the image can be displayed via the display 42.

The methods and systems described herein can be implemented in a numberof technologies but generally relate to imaging devices and processingcircuitry for performing the processes described herein. In oneembodiment, the processing circuitry (e.g., image processing circuitryand controller circuitry) is implemented as one of or as a combinationof: an application specific integrated circuit (ASIC), a fieldprogrammable gate array (FPGA), a generic array of logic (GAL), aprogrammable array of logic (PAL), circuitry for allowing one-timeprogrammability of logic gates (e.g., using fuses) or reprogrammablelogic gates. Furthermore, the processing circuitry can include acomputer processor and having embedded and/or external non-volatilecomputer readable memory (e.g., RAM, SRAM, FRAM, PROM, EPROM, and/orEEPROM) that stores computer instructions (binary executableinstructions and/or interpreted computer instructions) for controllingthe computer processor to perform the processes described herein. Thecomputer processor circuitry may implement a single processor ormultiprocessors, each supporting a single thread or multiple threads andeach having a single core or multiple cores.

Obviously, numerous modifications and variations are possible in lightof the above teachings. It is therefore to be understood that within thescope of the appended claims, embodiments of the present disclosure maybe practiced otherwise than as specifically described herein.

Embodiments of the present disclosure may also be as set forth in thefollowing parentheticals.

(1) An imaging apparatus, comprising: a plurality of detectorsconfigured to capture imaging data from a scan of an object; andcircuitry configured to divide the imaging data into a first setcorresponding to a first region of the object and a second setcorresponding to a second region of the object, wherein the secondregion is different than the first region, the first region has a firstmotion pattern, and the second region has a second motion patterndifferent than the first motion pattern, apply a first correctionprocess to the first set, apply a second correction process to thesecond set, the second correction process being different than the firstcorrection process, and generate an image including the first region andthe second region, the image being generated based on a result of thefirst correction process and the second correction process.

(2) The apparatus of (1), wherein the first motion pattern ispredictable and the second motion pattern is unpredictable.

(3) The apparatus of any of (1) to (2), wherein the first motion patternis unpredictable and the first correction process includes applying aconvolution kernel to the first set of the imaging data.

(4) The apparatus of any of (1) to (3), wherein the first motion patternis predictable and the first correction process includes usingself-navigation on the first set of the imaging data.

(5) The apparatus of any of (1) to (4), wherein the circuitry is furtherconfigured to reject regions of the imaging data having predeterminedcharacteristics.

(6) The apparatus of any of (1) to (5), further comprising at least onenavigator configured to obtain at least one of the first motion patternand the second motion pattern.

(7) The apparatus of any of (1) to (6), wherein dividing to obtain thefirst set and the second set is done in k-space based on signalsimilarity; applying at least one of the first correction process andthe second correction process includes using at least one convolutionkernel; and the at least one convolution kernel is estimated fromk-space using information from at least one center slice and slicesadjacent to each of the at least one center slice.

(8) The apparatus of any of (1) to (7), wherein dividing to obtain thefirst set and the second set are based on a coil sensitivity map.

(9) The apparatus of any of (1) to (8), wherein the imaging apparatus isan MRI apparatus and the detectors are coils.

(10) A correction method, comprising: dividing imaging data into a firstset corresponding to a first region of an object and a second setcorresponding to a second region of the object, wherein the secondregion is different than the first region, the first region has a firstmotion pattern, and the second region has a second motion patterndifferent than the first motion pattern; applying a first correctionprocess to the first set; applying a second correction process to thesecond set, the second correction process being different than the firstcorrection process; and generating an image including the first regionand the second region, the image being generated based on a result ofthe first correction process and the second correction process.

(11) The method of (10), wherein the first motion pattern ispredictable, and the second motion pattern is unpredictable.

(12) The method of any of (10) to (11), wherein the first motion patternis unpredictable and the first correction process includes applying aconvolution kernel to the first set of the imaging data.

(13) The method of any of (10) to (12), wherein the first motion patternis unpredictable and the first correction process includes usingself-navigation on the first set of the imaging data.

(14) The method of any of (10) to (13), further comprising rejectingregions of the imaging data having predetermined characteristics.

(15) The method of any of (10) to (14), further comprising at least onenavigator configured to obtain at least one of the first motion patternand the second motion pattern.

(16) The method of any of (10) to (15), wherein the dividing to obtainthe first set and the second set is done in k-space based on signalsimilarity; the applying of at least one of the first correction processand the second correction process includes using at least oneconvolution kernel; and the at least one convolution kernel is estimatedfrom k-space using information from at least one center slice and slicesadjacent to each of the at least one center slice.

(17) The method of any of (10) to (16), wherein dividing to obtain thefirst set and the second set are based on a coil sensitivity map.

(18) The method of any of (10) to (17), wherein the imaging apparatus isMRI data.

(19) A non-transitory computer-readable storage medium storingcomputer-readable instructions that, when executed by a computer, causethe computer to perform a method comprising dividing imaging data into afirst set corresponding to a first region of an object and a second setcorresponding to a second region of the object, wherein the secondregion is different than the first region, the first region has a firstmotion pattern, and the second region has a second motion patterndifferent than the first motion pattern, applying a first correctionprocess to the first set, applying a second correction process to thesecond set, the second correction process being different than the firstcorrection process, and generating an image including the first regionand the second region, the image being generated based on a result ofthe first correction process and the second correction process.

(20) The non-transitory computer-readable storage medium of (19),wherein the first motion pattern is predictable, and the second motionpattern is unpredictable.

Thus, the foregoing discussion discloses and describes merely exemplaryembodiments of the present disclosure. As will be understood by thoseskilled in the art, the present disclosure may be embodied in otherspecific forms without departing from the spirit thereof. Accordingly,the disclosure of the present disclosure is intended to be illustrative,but not limiting of the scope of the disclosure, as well as otherclaims. The disclosure, including any readily discernible variants ofthe teachings herein, defines, in part, the scope of the foregoing claimterminology such that no inventive subject matter is dedicated to thepublic.

1. An imaging apparatus, comprising: a plurality of detectors configuredto capture imaging data from a scan of an object; and circuitryconfigured to divide the imaging data into a first set corresponding toa first region of the object and a second set corresponding to a secondregion of the object, wherein the second region is different than thefirst region, the first region has a first motion pattern, and thesecond region has a second motion pattern different than the firstmotion pattern, apply a first correction process to the first set, applya second correction process to the second set, the second correctionprocess being different than the first correction process, and generatean image including the first region and the second region, the imagebeing generated based on a result of the first correction process andthe second correction process.
 2. The apparatus of claim 1, wherein thefirst motion pattern is predictable and the second motion pattern isunpredictable.
 3. The apparatus of claim 1, wherein the first motionpattern is unpredictable and the first correction process includesapplying a convolution kernel to the first set of the imaging data. 4.The apparatus of claim 1, wherein the first motion pattern ispredictable and the first correction process includes usingself-navigation on the first set of the imaging data.
 5. The apparatusof claim 1, wherein the circuitry is further configured to rejectregions of the imaging data having predetermined characteristics.
 6. Theapparatus of claim 1, further comprising at least one navigatorconfigured to obtain at least one of the first motion pattern and thesecond motion pattern.
 7. The apparatus of claim 1, wherein dividing toobtain the first set and the second set is done in k-space based onsignal similarity; applying at least one of the first correction processand the second correction process includes using at least oneconvolution kernel; and the at least one convolution kernel is estimatedfrom k-space using information from at least one center slice and slicesadjacent to each of the at least one center slices.
 8. The apparatus ofclaim 1, wherein dividing to obtain the first set and the second set arebased on a coil sensitivity map.
 9. The apparatus of claim 1, whereinthe imaging apparatus is an MRI apparatus and the detectors are coils.10. A correction method, comprising: dividing imaging data into a firstset corresponding to a first region of an object and a second setcorresponding to a second region of the object, wherein the secondregion is different than the first region, the first region has a firstmotion pattern, and the second region has a second motion patterndifferent than the first motion pattern; applying a first correctionprocess to the first set; applying a second correction process to thesecond set, the second correction process being different than the firstcorrection process; and generating an image including the first regionand the second region, the image being generated based on a result ofthe first correction process and the second correction process.
 11. Themethod of claim 10, wherein the first motion pattern is predictable, andthe second motion pattern is unpredictable.
 12. The method of claim 10,wherein the first motion pattern is unpredictable and the firstcorrection process includes applying a convolution kernel to the firstset of the imaging data.
 13. The method of claim 10, wherein the firstmotion pattern is unpredictable and the first correction processincludes using self-navigation on the first set of the imaging data. 14.The method of claim 10, further comprising rejecting regions of theimaging data having predetermined characteristics.
 15. The method ofclaim 10, further comprising at least one navigator configured to obtainat least one of the first motion pattern and the second motion pattern.16. The method of claim 10, wherein the dividing to obtain the first setand the second set is done in k-space based on signal similarity; theapplying of at least one of the first correction process and the secondcorrection process includes using at least one convolution kernel; andthe at least one convolution kernel is estimated from k-space usinginformation from at least one center slice and slices adjacent to eachof the at least one center slice.
 17. The method of claim 10, whereindividing to obtain the first set and the second set are based on a coilsensitivity map.
 18. The method of claim 10, wherein the imaging data isMRI data.
 19. A non-transitory computer-readable storage medium storingcomputer-readable instructions that, when executed by a computer, causethe computer to perform a method comprising dividing imaging data into afirst set corresponding to a first region of an object and a second setcorresponding to a second region of the object, wherein the secondregion is different than the first region, the first region has a firstmotion pattern, and the second region has a second motion patterndifferent than the first motion pattern, applying a first correctionprocess to the first set, applying a second correction process to thesecond set, the second correction process being different than the firstcorrection process, and generating an image including the first regionand the second region, the image being generated based on a result ofthe first correction process and the second correction process.
 20. Thenon-transitory computer-readable storage medium of claim 19, wherein thefirst motion pattern is predictable, and the second motion pattern isunpredictable.